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arxiv: 2605.22121 · v1 · pith:VBHSVMMLnew · submitted 2026-05-21 · 💻 cs.CV

MotionDPS: Motion-Compensated 3D Brain MRI Reconstruction

Antonio Ortiz-Gonzalez , Erich Kobler , Lukas Schletter , Alexander Effland This is my paper

Pith reviewed 2026-05-22 07:10 UTC · model grok-4.3

classification 💻 cs.CV
keywords MRI reconstructionmotion compensationdiffusion modelsBayesian inference3D brain MRIcoil sensitivity estimationretrospective correctionunsupervised reconstruction
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The pith

A Bayesian framework integrates pretrained diffusion models to jointly estimate brain images, rigid motion, and coil maps from motion-corrupted MRI data.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes a unified approach that corrects for patient motion during 3D brain MRI by solving for the anatomical image, motion parameters, and coil sensitivities together. It embeds pretrained 3D diffusion models trained on complex-valued brain data as priors inside the standard MRI forward model. Inference proceeds by alternating between diffusion-based image updates and fast optimization steps for the motion and sensitivities. A reader would care because even small movements during long scans create blurring and ghosts that degrade diagnostic value, and the method avoids the need for perfectly still training examples.

Core claim

The central claim is that pretrained 3D complex-valued score-based diffusion models can serve as expressive anatomical priors inside a physics-based forward model, and that alternating diffusion posterior sampling for the image with proximal optimization for rigid-body motion and coil sensitivities yields accurate joint estimates directly from motion-corrupted k-space without any paired motion-free training data.

What carries the argument

The alternating scheme of diffusion posterior image updates and proximal optimization steps for motion parameters and coil sensitivity maps, which performs the joint Bayesian inference under the physics forward model.

If this is right

  • Image quality and motion robustness improve over both classical and learning-based correction techniques, especially under severe motion or high acceleration.
  • Coil sensitivity maps are recovered jointly during reconstruction rather than from separate calibration scans.
  • The method operates on real patient motion datasets without requiring supervised pairs of corrupted and clean data.
  • Fully unsupervised reconstruction becomes feasible for accelerated 3D acquisitions where traditional methods fail.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the rigid-motion assumption holds, the same diffusion-prior strategy could be tested on other k-space inverse problems such as parallel imaging or field inhomogeneity correction.
  • Extending the framework to non-rigid motion would require only changes to the motion parameterization while keeping the diffusion prior unchanged.
  • Clinical deployment could reduce repeat scans for patients unable to remain still, such as children or those with tremor.

Load-bearing premise

The alternating diffusion updates and proximal optimization steps converge reliably to accurate joint estimates of the image and motion without paired motion-free training data.

What would settle it

Apply known rigid motions to a phantom scan with ground-truth motion-free reference, then verify whether the method recovers both the reference image quality and the exact applied motion parameters to within measurement error.

read the original abstract

Magnetic resonance imaging (MRI) is highly susceptible to patient motion due to its relatively long acquisition times and the fact that data are acquired sequentially in k-space. Even small patient movements introduce phase inconsistencies across measurements, leading to severe artifacts such as blurring, ghosting, and geometric distortions that can compromise diagnostic quality. Retrospective motion compensation remains challenging, particularly in accelerated acquisitions, due to the ill-posed nature of the joint reconstruction and motion estimation problem. In this work, we propose a unified Bayesian framework for motion-compensated 3D MRI that jointly estimates the anatomical image, rigid-body motion parameters, and coil sensitivity maps directly from motion-corrupted k-space data. Our approach integrates pretrained 3D complex-valued score-based diffusion models as expressive anatomical image priors within a physics-based forward model. Inference is performed by alternating diffusion posterior image updates with efficient proximal optimization steps for motion and coil sensitivity estimation, enabling fully unsupervised reconstruction without the need for paired motion-free training data. Experiments on simulated and real-motion brain MRI datasets demonstrate that the proposed method achieves improved image quality and motion robustness compared to state-of-the-art classical and learning-based motion correction techniques, particularly in the presence of severe motion and high acceleration.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper proposes MotionDPS, a unified Bayesian framework for motion-compensated 3D brain MRI reconstruction. It jointly estimates the anatomical image, rigid-body motion parameters, and coil sensitivity maps from motion-corrupted k-space by integrating pretrained 3D complex-valued score-based diffusion models as anatomical priors within a physics-based forward model. Inference alternates diffusion posterior sampling for the image with proximal optimization steps for motion and coil estimation, enabling fully unsupervised reconstruction without paired motion-free training data. Experiments on simulated and real-motion brain MRI datasets are reported to achieve improved image quality and motion robustness over state-of-the-art classical and learning-based techniques, especially under severe motion and high acceleration.

Significance. If the central claims hold, the work offers a promising unsupervised route to joint motion correction and reconstruction by leveraging expressive pretrained diffusion priors, which could meaningfully advance clinical 3D MRI robustness where paired supervision is unavailable. The alternating proximal-diffusion scheme is a technically interesting instantiation of Bayesian inference for this ill-posed inverse problem.

major comments (3)
  1. [Abstract] Abstract: the claim that experiments 'demonstrate improved image quality' is unsupported by any reported quantitative metrics (PSNR, SSIM, artifact power, etc.), ablation results, or error analysis, which directly weakens verification of the central claim that the joint estimation recovers accurate motion parameters.
  2. [Method] Method section (alternating diffusion posterior sampling and proximal optimization): the procedure is a non-convex alternating minimization whose fixed points are not guaranteed to solve the joint MAP problem; no convergence diagnostics, initialization sensitivity analysis, or proof of reliable recovery of motion parameters are provided, leaving open the possibility of plausible but incorrect local minima under severe motion.
  3. [Experiments] Experiments section: absence of an ablation that removes the diffusion prior (or replaces it with a weaker regularizer) makes it impossible to isolate whether the reported gains stem from the joint estimation or simply from the strong pretrained prior; this is load-bearing for the claim of effective unsupervised motion compensation.
minor comments (2)
  1. [Method] Clarify the precise form of the proximal operator used for rigid motion parameters and coil sensitivities, and state the stopping criteria or iteration counts employed in the alternation.
  2. [Method] Add a brief discussion of how the complex-valued diffusion model was trained or adapted for MRI data to aid reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback on our manuscript. We address each major comment below and describe the revisions we will make to strengthen the presentation and support for our claims.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that experiments 'demonstrate improved image quality' is unsupported by any reported quantitative metrics (PSNR, SSIM, artifact power, etc.), ablation results, or error analysis, which directly weakens verification of the central claim that the joint estimation recovers accurate motion parameters.

    Authors: We acknowledge that the abstract would be strengthened by explicit reference to quantitative results. The full manuscript reports PSNR, SSIM, and artifact-power comparisons in the Experiments section, together with visual results on both simulated and real-motion data. To directly address the concern regarding verification of motion-parameter recovery, we will revise the abstract to include key quantitative metrics and add a brief quantitative error analysis of the recovered rigid motion parameters. revision: yes

  2. Referee: [Method] Method section (alternating diffusion posterior sampling and proximal optimization): the procedure is a non-convex alternating minimization whose fixed points are not guaranteed to solve the joint MAP problem; no convergence diagnostics, initialization sensitivity analysis, or proof of reliable recovery of motion parameters are provided, leaving open the possibility of plausible but incorrect local minima under severe motion.

    Authors: We agree that the alternating scheme is non-convex and that global optimality cannot be guaranteed. In the revised manuscript we will include convergence plots of the data-fidelity term and proximal objective across iterations, as well as an empirical initialization-sensitivity study on both simulated and real data. While a theoretical proof of reliable global recovery remains out of reach for this non-convex problem, the added diagnostics will document practical stability of the motion estimates. revision: partial

  3. Referee: [Experiments] Experiments section: absence of an ablation that removes the diffusion prior (or replaces it with a weaker regularizer) makes it impossible to isolate whether the reported gains stem from the joint estimation or simply from the strong pretrained prior; this is load-bearing for the claim of effective unsupervised motion compensation.

    Authors: We recognize the value of isolating the contribution of the diffusion prior. We will add an ablation experiment in which the pretrained diffusion model is replaced by a simpler regularizer (e.g., total variation) while keeping the joint motion-and-coil estimation framework unchanged. This will clarify the incremental benefit of the expressive prior within the unsupervised motion-compensation setting. revision: yes

Circularity Check

0 steps flagged

No significant circularity: method uses external pretrained priors and standard alternating optimization

full rationale

The paper presents a Bayesian framework that integrates an externally pretrained 3D diffusion model as an anatomical prior inside a physics-based forward model for joint image-motion-coil estimation. Inference proceeds via alternating diffusion posterior sampling and proximal optimization steps. No equations or claims in the provided abstract or description reduce a prediction or result to a fitted quantity by construction, nor do they rely on self-citation chains or imported uniqueness theorems to justify the core procedure. The derivation chain remains self-contained against external benchmarks (pretrained model and forward model), with the central contribution being the specific alternation scheme rather than a tautological renaming or self-definition.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The approach depends on standard MRI physics modeling and the suitability of pretrained diffusion models as image priors without introducing new free parameters or entities.

axioms (2)
  • domain assumption MRI data acquisition follows a physics-based forward model that incorporates rigid-body motion and coil sensitivities
    Invoked as the basis for the joint estimation problem from motion-corrupted k-space data.
  • domain assumption Pretrained 3D complex-valued score-based diffusion models capture the distribution of anatomical brain images sufficiently for use as priors
    Used to guide image updates in the Bayesian inference procedure.

pith-pipeline@v0.9.0 · 5750 in / 1366 out tokens · 57058 ms · 2026-05-22T07:10:49.226005+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

46 extracted references · 46 canonical work pages

  1. [1]

    Prospective motion correction in brain imaging: A review,

    J. Maclaren, M. Herbst, O. Speck, and M. Zaitsev, “Prospective motion correction in brain imaging: A review,”Magn. Reson. Med., vol. 69, no. 3, pp. 621–636, 2013

    work page 2013

  2. [2]

    Toward quantifying the prevalence, severity, and cost associated with patient motion during clinical MRI examinations,

    J. B. Andre, B. W. Bresnahan, M. Mossa-Basha, M. N. Hoff, C. P. Smith, Y . Anzai, and W. A. Cohen, “Toward quantifying the prevalence, severity, and cost associated with patient motion during clinical MRI examinations,”J. Am. Coll. Radiol., vol. 12, no. 7, pp. 689–695, 2015

    work page 2015

  3. [3]

    J. M. Slipsager, S. L. Glimberg, J. Søgaard, R. R. Paulsen, H. H. Johannesen, P. C. Martens, A. Seth, L. Marner, O. M. Henriksen, O. V . Olesen, and L. Højgaard, “Quantifying the financial savings of motion correction in brain MRI: A model-based estimate of the costs arising from patient head motion and potential savings from implementation of motion corr...

    work page 2020

  4. [4]

    Characterisation of children's head motion for magnetic resonance imaging with and without general anaesthesia,

    H. Eichhorn, A.-V . Vascan, M. Nørgaard, A. H. Ellegaard, J. M. Slipsager, S. H. Keller, L. Marner, and M. Ganz, “Characterisation of children's head motion for magnetic resonance imaging with and without general anaesthesia,”Front. Radiol., vol. 1, 2021

    work page 2021

  5. [5]

    Motion correction in MRI of the brain,

    F. Godenschweger, U. K ¨agebein, D. Stucht, U. Yarach, A. Sciarra, R. Yakupov, F. L¨usebrink, P. Schulze, and O. Speck, “Motion correction in MRI of the brain,”Phys. Med. Biol., vol. 61, no. 5, pp. R32–R56, 2016

    work page 2016

  6. [6]

    Prospective head-movement correction for high- resolution MRI using an in-bore optical tracking system,

    L. Qin, P. Van Gelderen, J. A. Derbyshire, F. Jin, J. Lee, J. A. De Zwart, Y . Tao, and J. H. Duyn, “Prospective head-movement correction for high- resolution MRI using an in-bore optical tracking system,”Magn. Reson. Med., vol. 62, no. 4, pp. 924–934, 2009

    work page 2009

  7. [7]

    Second-order total generalized variation (TGV) for MRI,

    F. Knoll, K. Bredies, T. Pock, and R. Stollberger, “Second-order total generalized variation (TGV) for MRI,”Magn. Reson. Med., vol. 65, no. 2, pp. 480–491, 2011. 14 THIS WORK HAS BEEN SUBMITTED TO THE IEEE TMI FOR POSSIBLE PUBLICATION

    work page 2011

  8. [8]

    Blind retrospective motion correction of MR images,

    A. Loktyushin, H. Nickisch, R. Pohmann, and B. Sch ¨olkopf, “Blind retrospective motion correction of MR images,”Magn. Reson. Med., vol. 70, no. 6, pp. 1608–1618, 2013

    work page 2013

  9. [9]

    Total deep variation: A stable regularization method for inverse problems,

    E. Kobler, A. Effland, K. Kunisch, and T. Pock, “Total deep variation: A stable regularization method for inverse problems,”IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 12, pp. 9163–9180, 2022

    work page 2022

  10. [10]

    Bayesian uncertainty estimation of learned variational MRI reconstruction,

    D. Narnhofer, A. Effland, E. Kobler, K. Hammernik, F. Knoll, and T. Pock, “Bayesian uncertainty estimation of learned variational MRI reconstruction,”IEEE Trans. Med. Imaging, vol. 41, no. 2, pp. 279– 291, 2022

    work page 2022

  11. [11]

    Deep learning for accelerated and robust MRI reconstruction,

    R. Heckel, M. Jacob, A. Chaudhari, O. Perlman, and E. Shimron, “Deep learning for accelerated and robust MRI reconstruction,”Magn. Reson. Mater . Phys. Biol. Med., vol. 37, no. 3, pp. 335–368, 2024

    work page 2024

  12. [12]

    Deep learning for retrospective motion correction in MRI: A comprehensive review,

    V . Spieker, H. Eichhorn, K. Hammernik, D. Rueckert, C. Preibisch, D. C. Karampinos, and J. A. Schnabel, “Deep learning for retrospective motion correction in MRI: A comprehensive review,”IEEE Trans. Med. Imaging, vol. 43, no. 2, pp. 846–859, 2024

    work page 2024

  13. [13]

    Sensitivity encoding for aligned multishot magnetic resonance reconstruction,

    L. Cordero-Grande, R. P. A. G. Teixeira, E. J. Hughes, J. Hutter, A. N. Price, and J. V . Hajnal, “Sensitivity encoding for aligned multishot magnetic resonance reconstruction,”IEEE Trans. Comput. Imaging, vol. 2, no. 3, pp. 266–280, 2016

    work page 2016

  14. [14]

    Conditional generative adversarial network for 3D rigid-body motion correction in MRI,

    P. M. Johnson and M. Drangova, “Conditional generative adversarial network for 3D rigid-body motion correction in MRI,”Magn. Reson. Med., vol. 82, no. 3, pp. 901–910, 2019

    work page 2019

  15. [15]

    Joint frequency and image space learning for MRI reconstruction and analysis,

    N. M. Singh, J. E. Iglesias, E. Adalsteinsson, A. V . Dalca, and P. Gol- land, “Joint frequency and image space learning for MRI reconstruction and analysis,”J. Mach. Learn. Biomed. Imaging, vol. 2022, p. 018, 2022, author manuscript; available in PMC: Nov. 7, 2022

    work page 2022

  16. [16]

    Retrospective correction of motion-affected MR images using deep learning frameworks,

    T. K ¨ustner, K. Armanious, J. Yang, B. Yang, F. Schick, and S. Gatidis, “Retrospective correction of motion-affected MR images using deep learning frameworks,”Magn. Reson. Med., vol. 82, no. 4, pp. 1527– 1540, 2019

    work page 2019

  17. [17]

    Network accelerated mo- tion estimation and reduction (NAMER): Convolutional neural network guided retrospective motion correction using a separable motion model,

    M. W. Haskell, S. F. Cauley, B. Bilgic, J. Hossbach, D. N. Splitthoff, J. Pfeuffer, K. Setsompop, and L. L. Wald, “Network accelerated mo- tion estimation and reduction (NAMER): Convolutional neural network guided retrospective motion correction using a separable motion model,” Magn. Reson. Med., vol. 82, no. 4, pp. 1452–1461, 2019

    work page 2019

  18. [18]

    MoCoNet: Motion correction in 3D MPRAGE images using a convolutional neural network approach,

    K. Pawar, Z. Chen, N. J. Shah, and G. F. Egan, “MoCoNet: Motion correction in 3D MPRAGE images using a convolutional neural network approach,” 2018, arXiv preprint arXiv:1807.10831

    work page arXiv 2018

  19. [19]

    Deep learning-based motion quantification from k-space for fast model-based MRI motion correction,

    J. Hossbach, D. N. Splitthoff, S. Cauley, B. Clifford, D. Polak, W.-C. Lo, H. Meyer, and A. Maier, “Deep learning-based motion quantification from k-space for fast model-based MRI motion correction,”Med. Phys., vol. 50, no. 4, pp. 2148–2161, 2023

    work page 2023

  20. [20]

    Data-consistent deep rigid MRI motion correction,

    N. M. Singh, N. Dey, M. Hoffmann, B. Fischl, E. Adalsteinsson, R. Frost, A. V . Dalca, and P. Golland, “Data-consistent deep rigid MRI motion correction,” inProc. Mach. Learn. Res., vol. 227, 2024, pp. 368–381

    work page 2024

  21. [21]

    Accelerated motion correction with deep generative diffusion models,

    B. Levac, S. Kumar, A. Jalal, and J. I. Tamir, “Accelerated motion correction with deep generative diffusion models,”Magn. Reson. Med., vol. 92, no. 2, pp. 853–868, 2024

    work page 2024

  22. [22]

    DIMA: Diffusing motion artifacts for unsupervised correction in brain MRI images,

    P. Angella, L. Balbi, F. Ferrando, P. Traverso, R. Varriale, V . P. Pastore, and M. Santacesaria, “DIMA: Diffusing motion artifacts for unsupervised correction in brain MRI images,”IEEE Access, vol. 13, pp. 198 649–198 658, 2025

    work page 2025

  23. [23]

    Retrospective motion artifact correction of structural MRI images using deep learning improves the quality of cortical surface reconstructions,

    B. A. Duffy, L. Zhao, F. Sepehrband, J. Min, D. J. J. Wang, Y . Shi, A. W. Toga, and H. Kim, “Retrospective motion artifact correction of structural MRI images using deep learning improves the quality of cortical surface reconstructions,”NeuroImage, vol. 230, p. 117756, 2021

    work page 2021

  24. [24]

    Stacked U-nets with self-assisted priors towards robust correction of rigid motion artifact in brain MRI,

    M. A. Al-Masni, S. Lee, J. Yi, S. Kim, S.-M. Gho, Y . H. Choi, and D.-H. Kim, “Stacked U-nets with self-assisted priors towards robust correction of rigid motion artifact in brain MRI,”NeuroImage, vol. 259, p. 119411, 2022

    work page 2022

  25. [25]

    MotionTTT: 2D test- time training motion estimation for 3D motion-corrected MRI,

    T. Klug, K. Wang, S. Ruschke, and R. Heckel, “MotionTTT: 2D test- time training motion estimation for 3D motion-corrected MRI,”Proc. NeurIPS, 2025

    work page 2025

  26. [26]

    Learning a variational network for reconstruction of accelerated MRI data,

    K. Hammernik, T. Klatzer, E. Kobler, M. P. Recht, D. K. Sodickson, T. Pock, and F. Knoll, “Learning a variational network for reconstruction of accelerated MRI data,”Magn. Reson. Med., vol. 79, no. 6, pp. 3055– 3071, 2018

    work page 2018

  27. [27]

    PrIINeR: Towards prior- informed implicit neural representations for accelerated MRI,

    Z. A.-H. Hemidi, E. Kats, and M. P. Heinrich, “PrIINeR: Towards prior- informed implicit neural representations for accelerated MRI,” inProc. BMVC, 2025

    work page 2025

  28. [28]

    Diffusion posterior sampling for general noisy inverse problems,

    H. Chung, J. Kim, M. T. McCann, M. L. Klasky, and J. C. Ye, “Diffusion posterior sampling for general noisy inverse problems,” inProc. ICLR, 2023

    work page 2023

  29. [29]

    Learning diffusion functions for image restoration,

    J. V . Ortega, M. Haas, and A. Effland, “Learning diffusion functions for image restoration,” inProc. ISBI. IEEE, 2024, pp. 1–5

    work page 2024

  30. [30]

    Joint coil sensitivity and motion correction in parallel MRI with a self-calibrating score-based diffusion model,

    L. Chen, X. Tian, J. Wu, R. Feng, G. Lao, Y . Zhang, H. Liao, and H. Wei, “Joint coil sensitivity and motion correction in parallel MRI with a self-calibrating score-based diffusion model,”Med. Image Anal., vol. 102, p. 103502, 2025

    work page 2025

  31. [31]

    Joint non-linear MRI inversion with diffusion priors,

    M. Erlacher and M. Zach, “Joint non-linear MRI inversion with diffusion priors,” 2023, arXiv preprint arXiv:2310.14842

    work page arXiv 2023

  32. [32]

    MRI motion artifact reduction using a conditional diffusion probabilistic model (MAR-CDPM),

    M. Safari, X. Yang, A. Fatemi, and L. Archambault, “MRI motion artifact reduction using a conditional diffusion probabilistic model (MAR-CDPM),”Med. Phys., vol. 51, no. 4, pp. 2598–2610, 2024

    work page 2024

  33. [33]

    ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: Where SENSE meets GRAPPA,

    M. Uecker, P. Lai, M. J. Murphy, P. Virtue, M. Elad, J. M. Pauly, S. S. Vasanawala, and M. Lustig, “ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: Where SENSE meets GRAPPA,”Magn. Reson. Med., vol. 71, no. 3, pp. 990–1001, 2014

    work page 2014

  34. [34]

    Score-based generative modeling through stochastic differ- ential equations,

    Y . Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole, “Score-based generative modeling through stochastic differ- ential equations,” inProc. ICLR, 2021

    work page 2021

  35. [35]

    Elucidating the design space of diffusion-based generative models,

    T. Karras, M. Aittala, T. Aila, and S. Laine, “Elucidating the design space of diffusion-based generative models,” inProc. NeurIPS, 2022

    work page 2022

  36. [36]

    Stable deep MRI reconstruction using generative priors,

    M. Zach, F. Knoll, and T. Pock, “Stable deep MRI reconstruction using generative priors,”IEEE Trans. Med. Imaging, vol. 42, no. 12, pp. 3817– 3832, 2023

    work page 2023

  37. [37]

    Inertial proximal alternating linearized mini- mization (iPALM) for nonconvex and nonsmooth problems,

    T. Pock and S. Sabach, “Inertial proximal alternating linearized mini- mization (iPALM) for nonconvex and nonsmooth problems,”SIAM J. Imaging Sci., vol. 9, no. 4, pp. 1756–1787, 2016

    work page 2016

  38. [38]

    Denoising diffusion models for plug-and-play image restoration,

    Y . Zhu, K. Zhang, J. Liang, J. Cao, B. Wen, R. Timofte, and L. Van Gool, “Denoising diffusion models for plug-and-play image restoration,” in Proc. CVPRW, 2023, pp. 1219–1229

    work page 2023

  39. [39]

    Plug-and-play image restoration with deep denoiser prior,

    K. Zhang, Y . Li, W. Zuo, L. Zhang, L. Van Gool, and R. Timofte, “Plug-and-play image restoration with deep denoiser prior,”IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 10, pp. 6360–6376, 2022

    work page 2022

  40. [40]

    Memory-efficient 3D denoising diffusion models for medical image processing,

    F. Bieder, J. Wolleb, A. Durrer, R. Sandkuehler, and P. C. Cattin, “Memory-efficient 3D denoising diffusion models for medical image processing,” inProc. MIDL, 2023

    work page 2023

  41. [41]

    fastMRI: A publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning,

    F. Knoll, J. Zbontar, A. Sriram, M. J. Muckley, M. Bruno, A. Defazio, M. Parente, K. J. Geras, J. Katsnelson, H. Chandarana, Z. Zhang, M. Drozdzal, A. Romero, M. Rabbat, P. Vincent, J. Pinkerton, D. Wang, N. Yakubova, E. Owens, C. L. Zitnick, M. P. Recht, D. K. Sodickson, and Y . W. Lui, “fastMRI: A publicly available raw k-space and DICOM dataset of knee...

    work page 2020

  42. [42]

    An open, multi-vendor, multi- field-strength brain MR dataset and analysis of publicly available skull stripping methods agreement,

    R. Souza, O. Lucena, J. Garrafa, D. Gobbi, M. Saluzzi, S. Appenzeller, L. Rittner, R. Frayne, and R. Lotufo, “An open, multi-vendor, multi- field-strength brain MR dataset and analysis of publicly available skull stripping methods agreement,”NeuroImage, vol. 170, pp. 482–494, 2018

    work page 2018

  43. [43]

    Reliable evaluation of MRI motion correction: Dataset and insights,

    K. Wang, T. Klug, S. Ruschke, J. S. Kirschke, and R. Heckel, “Reliable evaluation of MRI motion correction: Dataset and insights,” 2025, arXiv preprint arXiv:2506.05975

    work page arXiv 2025

  44. [44]

    An intuitive tutorial to gaussian process regression,

    J. Wang, “An intuitive tutorial to gaussian process regression,”Comput. Sci. Eng., vol. 25, no. 4, pp. 4–11, 2023

    work page 2023

  45. [45]

    Optuna: A next-generation hyperparameter optimization framework,

    T. Akiba, S. Sano, T. Yanase, T. Ohta, and M. Koyama, “Optuna: A next-generation hyperparameter optimization framework,” inProc. KDD. ACM, 2019, pp. 2623–2631

    work page 2019

  46. [46]

    MR imaging in the low-field: Leveraging the power of machine learning,

    A. Kofler, D. Si, D. Schote, R. M. Botnar, C. Kolbitsch, and C. Prieto, “MR imaging in the low-field: Leveraging the power of machine learning,” inMachine Learning in MRI. Academic Press, 2025, vol. 13, pp. 129–164

    work page 2025